2013年中国海域船舶大气污染物排放对空气质量的影响

基于2013年中国海域船舶排放清单和空气质量数值模拟平台（WRF-SMOKE-CMAQ）,利用敏感性分析方法定量识别了中国海域船舶排放对沿海地区空气质量的影响特征. 结果表明：船舶排放对不同污染物的贡献特征空间差异显著,就SO₂、NO₂和PM_2.5而言,在沿海省份的年均贡献率分别为5%、7%、2%（1.1、1.7、0.9 μg·m^-3）,其中,珠三角和长三角地区受影响较大,SO₂、NO₂和PM_2.5贡献分别可达30%、31%、8%（7.7、9.2、2.7 μg·m^-3）和14%、13%、4%（3.7、5.3、1.9 μg·m^-3）.其次,船舶排放对空气质量影响季节性差异显著,尤其表现在PM_2.5的空间分布上,三大城市群中,船舶排放对污染物贡献的季节间最大差异倍数为SO₂（1.3~2.0）,NO₂（1.2~4.0）,PM_2.5（1.8~7.5）.值得关注的是,船舶排放对PM_2.5的浓度贡献表现出了明显的区域性（长距离传输）和复合性.本研究结果,一方面弥补了我国船舶排放对空气质量影响的量化特征认识不足,另一方面可为后续船舶排放的健康影响及控制费效分析等评估研究提供数据支撑.     

Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance: Part I—Ozone

This study examines ozone (O₃) predictions from the Community Multiscale Air Quality (CMAQ) model version 4.5 and discusses potential factors influencing the model results. Daily maximum 8-h average O₃ levels are largely underpredicted when observed O₃ levels are above 85 ppb and overpredicted when they are below 35 ppb. Using a clustering approach, model performance was examined separately for several different synoptic regimes. Under the most common synoptic conditions of a typical summertime Bermuda High setup, the model showed good overall performance for O₃, while associations have been identified here between other, less frequent, synoptic regimes and the O₃ overprediction and underprediction biases. A sensitivity test between the CB-IV and CB05 chemical mechanisms showed that predictions of daily maximum 8-h average O₃ using CB05 were on average 7.3% higher than those using CB-IV. Boundary condition (BC) sensitivity tests show that the overprediction biases at low O₃ levels are more sensitive to the BC O₃ levels near the surface than BC concentrations aloft. These sensitivity tests also show the model performance for O₃ improved when using the global GEOS-CHEM BCs instead of default profiles. Simulations using the newest version of the CMAQ model (v4.6) showed a small improvement in O₃ predictions, particularly when vertical layers were not collapsed. Collectively, the results suggest that key synoptic weather patterns play a leading role in the prediction biases, and more detailed study of these episodes are needed to identify further modeling improvements.     

MM5-SMOKE-CMAQ as a modeling tool for 8-h ozone regulatory enforcement: application to the state of Arizona

The Penn State/NCAR Mesoscale Meteorological Model 5 (MM5), Sparse Matrix Operator Kernal Emissions (SMOKE), and Community Multiscale Air Quality (CMAQ) modeling systems were employed to simulate ozone concentration distribution within the State of Arizona, in particular, Phoenix air basin, as supporting information to designate nonattainment areas of the U.S. Environmental Protection Agency's new 8-h ozone standard. In general, based on statistical comparisons between predictions and available (sparsely distributed) observations, the modeling system performed reasonably well for the Phoenix basin, thus proving it to be a useful tool for both regulatory as well as research applications. Detailed inspection, however, revealed a serious problem with respect to the details of the ozone distribution in that for some days the transition from downslope flow to upslope flow in the Phoenix basin was delayed in the model, causing the ozone distribution to show an unrealistic high-ozone bias toward the west valley. Implementation of a modified subgrid parameterization improved the time of transition, and hence the prediction of ozone and its precursor distributions. This study points to possible inadequacies of commonly used subgrid parameterizations in dealing with rapidly changing flow conditions such as morning (and evening) transitions.     

天津市各区县PM2.5污染工业行业贡献构成分析

应用MM5/CMAQ模型,选取1月和7月作为冬、夏两季的典型代表月份,采用源开关法,将天津市工业源分为8大类,模拟分析不同季节下,各类源对天津市各区县的PM2.5污染贡献.结果表明,热力供应是各区县冬季PM2.5污染的首要贡献源,贡献比例约为50%以上,而石油加工和化工制造是夏季各区县PM2.5污染贡献的一个重要来源,贡献比例从冬季的7%上升到夏季的23%;在冬夏两季,黑色金属冶炼和水泥制造是构成天津市PM2.5污染的重要来源,合计分别约占工业污染贡献比重的30%和65%;此外,除蓟县外,电力生产在冬夏两季均不是各区县PM2.5的主要贡献来源.从具体区县看,水泥制造对河北、北辰、红桥等区县有较大影响,化工制造对宁河及滨海新区有较大影响,电力生产则对蓟县有较大影响.     

The impact of chemical lateral boundary conditions on CMAQ predictions of tropospheric ozone over the continental United States

A sensitivity study is performed to examine the impact of lateral boundary conditions (LBCs) on the NOAA-EPA operational Air Quality Forecast Guidance over continental USA. We examined six LBCS: the fixed profile LBC, three global LBCs, and two ozonesonde LBCs for summer 2006. The simulated results from these six runs are compared to IONS ozonesonde and surface ozone measurements from August 1 to 5, 2006. The choice of LBCs can affect the ozone prediction throughout the domain, and mainly influence the predictions in upper altitude or near inflow boundaries, such as the US west coast and the northern border. Statistical results shows that the use of global model predictions for LBCs could improve the correlation coefficients of surface ozone prediction over the US west coast, but could also increase the ozone mean bias in most regions of the domain depending on global models. In this study, the use of the MOZART (Model for Ozone And Related chemical Tracers) prediction for CMAQ (Community Multiscale Air Quality) LBC shows a better surface ozone prediction than that with fixed LBC, especially over the US west coast. The LBCs derived from ozonesonde measurements yielded better O₃ correlations in the upper troposphere.     

珠江三角洲秋季典型气溶胶污染的过程分析

为了解大气中各物理和化学过程对气溶胶浓度的贡献情况,利用Models-3/CMAQ模式系统对珠江三角洲(以下简称珠三角)秋季典型气溶胶污染进行研究.模拟时间是2012年10月,期间珠三角主要受高压系统的控制,在17日冷锋过境前后高压天气形势发生转变,风向从东北风转为偏东风.结果表明,珠三角秋季PM2.5浓度呈现西高东低的水平分布特征,随着高度的上升浓度高值中心也向西南方向偏移;受大气边界层高度的影响,陆地上PM2.5输送高度呈现白天高夜晚低的变化特征;过程分析结果表明源排放,水平输送和垂直输送是影响近地面PM2.5浓度变化的主要过程;本地污染物排放是城市中心(广州站)PM2.5浓度升高的主要原因,而在下风向位置(江门站)外来污染物的水平输送过程是PM2.5的最主要来源.     

Process analysis of PM2.5 pollution events in a coastal city of China using CMAQ

US EPA's Community Multiscale Air Quality modeling system(CMAQ) with Process Analysis tool was used to simulate and quantify the contribution of individual atmospheric processes to PM_(2.5) concentration in Qingdao during three representative PM_(2.5) pollution events in the winter of 2015 and 2016. Compared with the observed surface PM_(2.5) concentrations, CMAQ could reasonably reproduce the temporal and spatial variations of PM_(2.5) during these three events. Process analysis results show that primary emissions accounted for 72.7%–93.2% of the accumulation of surface PM_(2.5) before and after the events.When the events occurred, primary emissions were still the major contributor to the increase of PM_(2.5) in Qingdao, however the contribution percentage reduced significantly,which only account for 51.4%–71.8%. Net contribution from horizontal and vertical transport to the accumulation of PM_(2.5) was also positive and its percentage increased when events occurred. Only 1.1%–4.6% of aerosol accumulation was due to PM processes and aqueous chemical processes before and after events. When the events occurred,contribution from PM processes and aqueous chemistry increased to 6.0%–11.8%. Loss of PM_(2.5) was mainly through horizontal transport, vertical transport and dry deposition, no matter during or outside the events. Wet deposition would become the main removal pathway of PM_(2.5), when precipitation occurred.     

Development and case study of a new-generation model-VAT for analyzing the boundary conditions influence on atmospheric mercury simulation

Atmospheric models are essential tools to study the behavior of air pollutants. To interpret the complicated atmospheric model simulations, a new-generation Model Visualization and Analysis Tool (Model-VAT) has been developed for scientists to analyze the model data and visualize the simulation results. The Model-VAT incorporates analytic functions of conventional tools and enhanced capabilities in flexibly accessing, analyzing, and comparing simulated results from multi-scale models with different map projections and grid resolutions. The performance of the Model-VAT is demonstrated by a case study of investigating the influence of boundary conditions (BCs) on the ambient Hg formation and transport simulated by the CMAQ model over the Pearl River Delta (PRD) region. The alternative BC options are taken from (1) default time-independent profiles, (2) outputs from a CMAQ simulation of a larger nesting domain, and (3) concentration files from GEOS-Chem (re-gridded and re-projected using the Model-VAT). The three BC inputs and simulated ambient concentrations and deposition were compared using the Model-VAT. The results show that the model simulations based on the static BCs (default profile) underestimates the Hg concentrations by ~6.5%, dry depositions by ~9.4%, and wet depositions by ~43.2% compared to those of the model-derived (e. g. GEOS-Chem or nesting CMAQ) BCs. This study highlights the importance of model nesting approach and demonstrates that the innovative functions of Model-VAT enhances the efficiency of analyzing and comparing the model results from various atmospheric model simulations.

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电力行业多污染物协同控制的环境效益模拟

为定量分析电力行业多污染物协同控制与区域复合型大气污染之间的定量关系,评估不同控制情景下的环境质量效益,应用CMAQ空气质量模型分别对2008年基准排放情景、2015年和2020年目标控制情景的硫、氮沉降及PM_2.5污染状况进行模拟. 结果表明:2015年和2020年我国陆地硫沉降总量将由2008年的678.87×104 t分别降至602.02×104和578.26×104 t,降幅分别为11.32%和14.82%,平均每减排1 t SO₂可减少0.2～0.3 t硫沉降;2015年和2020年的陆地氮沉降总量将由2008年的1 064.67×104 t分别降至1 042.02×104和1 037.06×104 t,仅分别降低了2.13%和2.59%,但重度氮沉降区域明显缩小,2015年和2020年氮沉降强度大于5 g/m2的区域将比2008年分别降低17.12%和22.01%;2015年和2020年ρ(PM_2.5)年均值超过GB 3095─2012《环境空气质量标准》二级标准(35 μg/m3)的国土面积分别仍将高达289.14×104和286.68×104 km2,与2008年(298.99×104 km2)相比,降幅分别为3.29%和4.12%,但重污染区域显著减少,并且ρ(PM_2.5)年均值超过70 μg/m3的区域将比2008年减少9.31%和12.41%.      

基于CMAQ空气质量模型研究机动车对济南市空气质量的影响

为研究济南市机动车排气对城市区域空气质量的影响,利用环境空气质量监测站点（简称"1号站点"）和路边机动车尾气监测站点（简称"2号站点"）的在线数据,以及基于4种模拟情景的CMAQ空气质量模型预测数据,研究了济南市城市区域大气污染物质量浓度变化规律及不同机动车车型对6种常规大气污染物的贡献.结果表明:①在采暖季,1号站点ρ（PM_2.5）、ρ（PM₁₀）、ρ（NO₂）、ρ（CO）、ρ（O₃）和ρ（SO₂）月均值分别为435 μg/m3、702 μg/m3、84.2 μg/m3、6.8 mg/m3、4.5 μg/m3和92 μg/m3.②2015年12月24日（灰霾天）,1号站点ρ（CO）、ρ（PM_2.5）和ρ（PM₁₀）均明显升高,ρ（SO₂）、ρ（O₃）和ρ（NO₂）均变化不明显.2个监测站点中ρ（NO₂）和ρ（PM₁₀）均呈双峰趋势,2个峰值出现的时间与上、下班高峰期基本一致.除ρ（O₃）和ρ（SO₂）达GB 3095-2012《环境空气质量标准》二级标准外,其他污染物均超过GB 3095-2012二级标准限值,采暖季大气污染特征为颗粒物型污染.③机动车对研究区域NO₂和PM₁₀贡献率较大,其中,小型车对CO、NO₂、PM₁₀和PM_2.5贡献率最大,其贡献率分别为85.7%、50.1%、53.4%和52.8%.机动车排放源能降低空气中ρ（O₃）,其总贡献率为-25.5%,其中大型车、中型车、小型车对O₃的贡献率分别为-8.8%、-2.7%和-8.9%.灰霾天下不同机动车车型对空气中污染物质量浓度的总贡献率均比采暖季大.研究显示,济南市采暖季大气污染特征为颗粒物型污染,机动车排放源对空气中NO₂和PM_2.5有较大贡献.